In order to study the quantitative evaluation of the effectiveness of intelligent vehicle under test conditions, the risk model based on driving risk field was improved, and the complexity degree of each elements of driving environment was defined. Considering risk degree and complexity degree coverage, maximum and distribution of user and test conditions, an evaluation model of user and test conditions based on risk degree and complexity degree was constructed. The validity of three tests was analyzed and evaluated by examples. The results showed that the effectiveness index could be used to evaluate the effectiveness of the test conditions quantitatively.

To research the quantitative evaluation of the effectiveness of intelligent vehicles from user operating conditions to test operating conditions in proving ground, a matching model and evaluation model of intelligent vehicle test condition and user condition was presented on the basis of theory of human-vehicle-road cooperative driving risk field by comprehensively considering the acceleration coefficient, risk coverage, maximum risk, and risk distribution of user conditions and test site conditions. With the vehicle following scene taken as an example and with the JT/T 1242-2019, GB/T 33577-2017, and ISO 22839 standards used as reference, the validity of the actual operating condition data of users and the operating condition data of the test site was analyzed and evaluated by using the correlation evaluation model. Results showed that the research results can be used to quantitatively evaluate the effectiveness of different intelligent vehicle test conditions.